Evaluating the Ecological Sustainability
of a Pinyon-Juniper Grassland
Ecosystem in Northern Arizona
Reuben Weisz1*, Jack Triepke1, Don Vandendriesche2,
Mike Manthei3, Jim Youtz1, Jerry Simon1, Wayne Robbie1
Abstract—In order to develop strategic land management plans, managers must assess current and future ecological conditions. Climate change has expanded the need
to assess the sustainability of ecosystems and predict their conditions under different
climate change and management scenarios using landscape dynamics simulation
models. We present a methodology for developing a state-and-transition model
(STM) with the Vegetation Dynamics Development Tool (VDDT), using outputs from
the Forest Vegetation Simulator (FVS). Preside, a recently developed accessory to the
FVS program, is used to process and report FVS outputs in terms of succession probabilities and residence times for each STM. We’ve applied these tools with a case
study based on the pinyon-juniper grassland ecosystem in northern Arizona. After
applying local probability values for natural growth, contemporary fire, insect and
disease, and management activities, VDDT simulations were conducted to project
future ecosystem conditions including carbon accounting. Finally, we also describe
how these models can be retooled with FVS support to reflect the effects of climate
change so that managers can consider adaptation and mitigation strategies.
Introduction
The objective of this paper is to illustrate through a case study how State-andTransition Models (STMs) can be developed and used to evaluate the ecological
sustainability of ecosystems.
Projecting transitions in vegetation states (composition and structure) over
time facilitates evaluating the ecological sustainability of ecosystems. Vegetation
states can change in “the absence of disturbance” through natural regeneration,
growth, competition and mortality; change also can result from disturbances and
other discrete events in time such as fire, management activity, insect and disease
outbreaks, etc. To facilitate projecting the effects of the interactions of these agents
of change, landscape STMs such as the Vegetation Dynamics Development Tool
(VDDT) developed by ESSA Technologies Limited (2006) can be used to quantify
the dynamics of vegetation change (He 2008).
Ecological sustainability analysis evaluates both ecosystems (ecosystem diversity) and their associated species (species diversity). A guiding principle for
ecosystem management (FEMAT 1993) is to use ecosystem reference conditions,
the range of variation, as an inference of ecological sustainability to enable the
persistence of ecosystem function and species diversity. In this paper, we focus
on vegetation diversity and related ecological processes such as fire, and apply a
case study assessing the ecological sustainability of the pinyon-juniper grassland
ecosystem on the Coconino National Forest (NF) in northern Arizona.
USDA Forest Service Proceedings RMRS-P-61. 2010.
In: Jain, Theresa B.; Graham, Russell T.;
and Sandquist, Jonathan, tech. eds.
2010. Integrated management of
carbon sequestration and biomass
utilization opportunities in a changing climate: Proceedings of the 2009
National Silviculture Workshop; 2009
June 15-18; Boise, ID. Proceedings
RMRS-P-61. Fort Collins, CO: U.S.
Department of Agriculture, Forest
Service, Rocky Mountain Research
Station. 351 p.
1
USDA Forest Service, Southwestern
Region, Albuquerque, NM, 87102,
USA.
2 USDA
Forest Service, Forest Management Service Center, Fort Collins, CO,
80526, USA.
3 Coconino National Forest, Flagstaff,
AZ, 86001, USA.
* Corresponding Author: Tel.: 01 505
842 3217; email: rweisz@fs.fed.us.
321
Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie
Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona
Methods
Framing the Analysis
We stratified the Coconino NF by potential natural vegetation types (PNVTs)
(Schussman and Smith 2006), a coarse ecosystem framework defined by site
potential and historic fire regimes, that provides a basic framework for analyzing
ecosystem diversity. Although the same process was used on each PNVT, this
paper documents the analysis process conducted on the 122,086 hectare (301,675
acres) pinyon-juniper grassland PNVT.
The pinyon-juniper grassland type occurs across the States of Arizona and
New Mexico, in what was historically open woodlands with grassy understories
(Ffolliott and Gottfried 2002). On the Coconino NF tree species include twoneedle
pinyon (Pinus edulis Engelm.), oneseed juniper (Juniperus monosperma (Engelm.)
Sarg.), Utah juniper (Juniperus osteosperma (Torr.) Little), and alligator juniper
(Juniperus deppeana Steud.). On reference sites, native understories are made
up of predominantly cool season perennial grasses including muttongrass (Poa
fendleriana (Steud.) Vasey), squirreltail (Elymus elymoides (Raf.) Swezey ssp.
brevifolius (J.G. Sm.) Barkworth) and western wheatgrass (Pascopyrum smithii
(Rydb.) A. Löve), with both annual and perennial forbs, while shrubs are absent
or scarce (<1 percent cover)(Miller and others 1995). Contemporary understories
often include invasive grasses such as cheatgrass (Bromus tectorum L.) and a
dominance of warm season species such as blue grama (Bouteloua gracilis (Willd.
ex Kunth) Lag. ex Griffiths), and have uncharacteristically high shrub cover. This
pinyon-juniper woodland type is typically found on sites with well-developed
and moderately deep soils with loam and clay loam surface textures. Soil orders
include mollisols derived from basaltic parent materials, andisols formed from
cinder deposits and alfisols developed from sedimentary sources. Climate is
characterized by a seasonal distribution of precipitation of which over half occurs
between the months of April through September, with an annual rainfall ranging
from 15-18 inches.
Information on the historic condition of this type is sparse. The ability to
reconstruct historic stand structure and fire chronologies in pinyon-juniper is
problematic, so the role of fire and the resulting vegetation structure is often
speculative (Jacobs 2008). However, site productivity suggests that the development of a grass and fine fuels layer would have supported frequent fire, open
forest dynamics, and perhaps uneven-aged conditions (Gottfried 2003).
We described historic (reference), current, and future structural conditions according to standard classification schemes based on average tree size (diameter)
and canopy cover class (Brohman and Bryant 2005; table 1). Due to disparities
in historic and current condition references, and how they were developed, it
was necessary to develop crosswalks to normalize across references and enable
comparisons between historic and current conditions. For instance, the U.S.
Department of Agriculture (USDA), Forest Service mid-scale mapping, used to
depict current conditions (Mellin and others 2008), uses a canopy cover break
of 30 percent to distinguish open and closed, versus the LANDFIRE model
­(Havlina 2005) that employs a 40 percent break. We portrayed historic, current,
and future composition conditions according to a southwestern regional classification of existing vegetation based on dominance types (Triepke and others
2005). Dominance types, defined by the relative abundance and dominance of
tree species, are similar to Society of American Foresters or Society for Range
Management cover types (Eyre 1980; Shiflet 1994), but are keyable, exhaustive,
and mutually exclusive.
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USDA Forest Service Proceedings RMRS-P-61. 2010.
Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona
Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie
Table 1—Crosswalk to facilitate comparison of historic, current, and future conditions of the pinyon-juniper grassland ecosystem.
Reference Condition
LANDFIRE RA JUPI1 Model
State Meana Description
Current Condition
USDA FS R3 Mid-Scale EV Map
Dominance Unit
Structureb
Future Trends
USDA FS R3 PJ Grassland Model
State
Description
A
5%
Post Non-tree: Recently
Grass-forb-shrub
A
replacement
burned and all shrub,
grass, and forb types
Seed/sap-open
B
C
25%
Mid-open
—
Seed/sap-closed
E
All pinyon, juniper,
and mixed shade
Small-open
C
intolerant tree Med to very
D
50%
Late-open
dominance types large-open
D
occurring within the
B
10% Mid development
pinyon-juniper
Small-closed
F
closed
grassland PNVT
E
10%
Late-closed
—
Medium to
very large-
G
closed
GFB/SHR
SSO
SSC
SMO
MVO
SMC
MVC
a Average
proportion of the landscape during the reference period (circa 880-1880 (Schussman and Smith 2006)).
Size classes based on diameter at breast height for forest tree species and diameter at root collar for woodland species: seedling/sapling
(< 13cm), small (13–24.9cm), medium (25–50cm), and very large (>50cm): overstory cover classes are sparse (<10% tree canopy cover), open
(10 – 29.9% cover), and closed (>29.9% cover).
b
Specific combinations of dominance type, size class, and canopy cover that are
characteristic to each PNVT are expressed in terms of vegetation states identified for each PNVT, and configured in PRESIDE (Process RESIDEnce Times),
a recently developed ancillary program (Vandendriesche 2009) to the Forest
Vegetation Simulator (FVS) model (Dixon 2002). Each vegetative state represents
an important phase in the ecosystem dynamics of a PNVT. The historic pinyonjuniper grassland ecosystem has been described (LANDFIRE 2007) as a five-state
model that includes a grass-forb state (A), two open forest states (C and D), and
two closed forest states (B and E) (table 1). Frequent surface fires maintained
the forest in these reference conditions. Ecological process reflects the ability
of natural and anthropogenic events such as fire, forest insect and disease, and
resource management activities to alter vegetation composition and structure
and, in turn, wildlife habitat and species diversity (Perry and Amaranthus 1997).
Along with site potential, the characteristic frequency and severity of fire are
differentia of the PNVT classes themselves.
Describing Reference Conditions
The Vegetation Dynamic Development Tool (VDDT, ESSA 2006) has been
used by the National LANDFIRE program (Ryan and others 2006) and others
such as the Nature Conservancy (TNC) (Schussman and Smith 2006) to develop
state-and-transition models that describe reference conditions. The VDDT software moves cells (representing a unit of area) from one state to another based on
a set of “transitions.” Traditionally, “deterministic transitions” describe succession (aging) in the absence of disturbance. Probabilistic transitions reflect the
quantitative assessments of discrete natural and anthropogenic events including
fire, insects, diseases, grazing, harvesting, and severe weather events. Each
probabilistic transition typically has three characteristics that define its pathway:
1) its return frequency or probability, 2) its severity or impact on vegetation, and
3) the destination state in which the cell will reside after transition.
USDA Forest Service Proceedings RMRS-P-61. 2010.
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Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie
Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona
We retooled these models to project future conditions by replacing historic probabilities and transitions with contemporary transitions and attendant frequencies
that reflect current land management. We also added contemporary and possible
future vegetative states. We detail the development of the pinyon-juniper grassland
model below. Reference condition descriptions and models typically are based
on peer-reviewed journal articles as well as published conference proceedings,
reports, theses, dissertations, and book chapters along with some consideration
of professional judgment provided by model developers. In contrast, the models
that we developed for projecting future conditions were more empirical, using
Forest Inventory and Analysis (FIA) data, FVS simulation runs, and related
software tools.
Describing Current Conditions
We mapped PNVTs using Terrestrial Ecosystem Survey (TES) data for the
Coconino NF (Miller and others 1995). The TES is a terrestrial ecological unit
inventory that formulates map units based on similarities in climate, soils, landform, and potential vegetation at the map scale of 1:24,000 (Winthers and others
2005). Among the map unit attributes, disclimax classes (zootic, fire) indicate
historic disturbance regime, making TES map data the best available resource
for PNVT mapping.
In 2004, the Southwestern Region initiated mid-scale mapping of existing
vegetation at 1:100,000 across all National Forests and Grasslands (Mellin and
others 2008). This mapping includes the three principle existing vegetation map
components previously mentioned—dominance type, size class, canopy cover.
With the description of vegetation states (table 1), these map data allowed for the
quantitative analysis of current conditions within each PNVT. We intersected
PNVT mapping in GIS with the existing dominance type, size class, and canopy
cover layers from mid-scale vegetation mapping products to produce tabular
summaries of current conditions within each PNVT class. These summaries
were in turn synthesized to give hectares and percent of each vegetation state
within each PNVT. We then compared these percents to historic and projected
conditions for the ecosystem.
Along with each condition reported (historic, current, or projected), we calculated ecosystem condition class values using the same equation employed
by LANDFIRE to compute Fire Regime Condition Class (FRCC) (Hann and
others 2005). But unlike FRCC, which provides percentages for each departure
class (1, 2, or 3), our own ecosystem condition class (ECC) provides one overall
departure rating for a given analysis area (Weisz and others 2009). The ECC is
computed for each comparison, either current vs. reference condition, or projected
vs. reference condition (table 2) based on the departure of all states in total from
their reference conditions. In each calculation, the sum of the lesser of percent
values for each state, either reference or current, is subtracted from 100 to provide
one overall departure index on a scale of 0 percent to 100 percent, higher values
representing more departed conditions. From there, three classes make up the
ECC rating system:
• ECC 1 (within reference condition) represents departure index values < 33;
• ECC 2 (moderately departed) represents departure index values >33 and < 66;
and
• ECC 3 (severely departed) represents departure index values > 66.
Recently developed FRCC map data for LANDFIRE map zones in Arizona
(LANDFIRE 2008) corroborate our findings, as do regional studies of these
systems.
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Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona
Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie
Table 2—Calculation of departure and ecosystem condition class based on
the disparity between reference and current conditions for the PJ grassland
ecosystem on the Coconino NF.
Reference condition
State
Description
A
C
D
B
E
Post replacement
Mid-open
Late-open
Mid development closed
Late-closed
Meana
5%
25%
50%
10%
10%
Current condition
Proportion
Calculationb
23%
14%
27%
17%
19%
Sum
Departure index value = 100% - Sum
Ecosystem condition class 1 (0–33), 2 (34–65), 3 (66+)
5%
14%
27%
10%
10%
66%
34
“2”
a Average
proportion of the landscape during the reference period (circa 880-1880 (Schussman
and Smith 2006)).
b Lesser of reference condition and current condition.
Projecting Future Conditions
Retooling models—Typically reference conditions models are based on a
survey of the literature, supplemented by empirical data as well as expert opinion (LANDFIRE 2008). Often these models are applicable to a large map zone
or to a large region like Arizona and New Mexico (Gori and Bate 2007; Havlina
2005). To retool these models to project conditions under existing or proposed
management schemes, managers can modify reference condition models to:
1) include new states or modified states that reflect vegetation classes that did
not exist under reference conditions; 2) incorporate current and projected natural
and anthropogenic processes; and 3) incorporate current and projected transition
probabilities. We illustrate by example how the Coconino NF retooled the pinyonjuniper grassland reference condition model for this purpose (see below), with
the assumption of no climate change. Carbon accounting was also provided using
the carbon extension of FVS (Havis and Crookston 2008; Hoover and Rebain
2008). The carbon extension provides values for dead and live standing trees,
and dead and live belowground tree tissue (Hoover and Rebain 2008). Standard
values are provided for carbon held in herbs and shrubs, downed wood, and litter
and duff, based on similarly measured plant communities. The paper concludes
with a description of how the model could be retooled in the future to consider
climate change.
New or modified states—Typically, models for current and projected conditions contain as many or more states than reference condition models. In the
case of the pinyon-juniper grassland PNVT, we developed a seven-state model
to describe current conditions in contrast to the reference conditions model that
had five states. Table 1 illustrates how we cross walked the reference conditions
states with the current states.
Quantifying current transitions—To retool reference condition models to
reflect contemporary processes, four steps are followed: 1) identify the contemporary transitions; 2) replace reference transitions with contemporary ones
(tables 3, 4a and 4b); 3) model future conditions; and, 4) interpret the results. Each
contemporary transition is identified in terms of its type, transition class (groups
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Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie
Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona
Table 3—Canopy cover and fire mortality proportion table.
Canopy Cover / Fire Mortality Proportion Table
Beginning Canopy Cover
Fire Severity Class
Class
9% → sparse (0 – 10%)
non-lethal
10 – 30% (open)
Ending Percentage by Canopy
Cover Classes
91% → open (10 – 30%)
55% → sparse (0 – 10%)
mixed severity
45% → open (10 – 30%)
stand replacement
100% → sparse (0 – 10%)
16% → open (10 – 30%)
non-lethal
84% → closed (30 – 60%)
2% → sparse (0 – 10%)
30 – 60% (closed)
79% → open (10 – 30%)
mixed severity
19% → closed (30 – 60%)
stand replacement
87% → sparse (0 – 10%)
13% → open (10 – 30%)
of transition types), frequency, and effects. We used four transition classes in
our current model: wildland fire, management activities, insect and disease, and
natural growth transitions in the absence of disturbance. Transition types within
each transition class may have unique frequencies and effects unto themselves.
The management activities transition class contains, for example, mechanical
thinning, prescribed burning, etc.
Wildland fire transitions—We used LANDFIRE definitions of fire severity
based on how much overstory canopy mortality would occur during a wildland
fire: nonlethal (or low severity), <25 percent mortality; mixed severity fire, 25
percent to 75 percent mortality; and stand replacement fire, >75 percent mortality (Hann and others 2005). We generated fire frequencies for each of the
transition classes using local fire history data on the planning unit for the period
1988 through 2006. Spatial data was available for approximately five wildland
fires greater than 40 hectares (16 acres) in size for the period 1960 to 2005. Fire
mortality mapping was available for three incidents including the Lizard (2003),
Mormon (2003), and Jacket (2004) fires. For other fires that occurred after 1975,
fire officials provided estimates of the percentage of non-lethal, mixed severity,
and stand replacement fire that occurred. We corroborated fire mortality for the
fires using orthophotos in GIS, estimating fire extent and mortality based on
patterns of top-kill and regeneration.
We summarized these results as average annual probabilities per hectare for
each fire type: nonlethal fire (0.0002), mixed severity fire (0.0021), and stand
replacing fire (0.0032) and assigned these probabilities to each model state (table
4a). The mixed severity and stand replacement probabilities can be attributed to
significant Pinyon Ips bark beetle activity since 1996. The effects of a fire on a
cell within the model depend on pre-fire canopy cover and the severity of the fire
(the fire mortality class; table 3).
Management activity transitions—We quantified management activities using the Forest Activity Tracking System (FACTS) database (M. Pitts, unpublished
data). We queried all activities recorded on the planning unit from 1988 through
2006, and then eliminated activities that did not affect broad-scale vegetation
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Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona
Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie
Table 4a—Pinyon-juniper grassland natural and anthropogenic disturbance transitions expressed
as the average annual probability per hectare per year.
rom State Code: Acronym: Description:
F
Transition Type
Probability
A: GFB: Grass/Forb/Brush
Nonlethal Fire
0.0002
Stand Replacing Fire
0.0032
B: SSO: Seed/Sap, Open
All Regeneration
0.0011
Insect and Disease
0.0100
Mixed Severity Fire
0.0021
Mixed Severity Fire
0.0021
Nonlethal Fire
0.0002
Nonlethal Fire
0.0002
Stand Replacing Fire
0.0032
C: SMO:Small, Open
All Regeneration
0.0011
Insect and Disease
0.0100
Mixed Severity Fire
0.0021
Mixed Severity Fire
0.0021
Nonlethal Fire
0.0002
Nonlethal Fire
0.0002
Stand Replacing Fire
0.0032
D: MVO:Medium to Very Large Open
All Regeneration
0.0011
Insect and Disease
0.0100
Mixed Severity Fire
0.0021
Mixed Severity Fire
0.0021
Nonlethal Fire
0.0002
Nonlethal Fire
0.0002
Stand Replacing Fire
0.0032
E: SSC: Seed/Sap Closed
All Regeneration
0.0011
Insect and Disease
0.0100
Mixed Severity Fire
0.0021
Mixed Severity Fire
0.0021
Mixed Severity Fire
0.0021
Nonlethal Fire
0.0002
Nonlethal Fire
0.0002
Stand Replacing Fire
0.0032
Stand Replacing Fire
0.0032
F: SMC: Small, Closed
All Regeneration
0.0011
Insect and Disease
0.0100
Mixed Severity Fire
0.0021
Mixed Severity Fire
0.0021
Mixed Severity Fire
0.0021
Nonlethal Fire
0.0002
Nonlethal Fire
0.0002
Stand Replacing Fire
0.0032
Stand Replacing Fire
0.0032
G: MVC:Medium to Very Large Closed
All Regeneration
0.0011
Insect and Disease
0.0100
Mixed Severity Fire
0.0021
Mixed Severity Fire
0.0021
Mixed Severity Fire
0.0021
Nonlethal Fire
0.0002
Nonlethal Fire
0.0002
Stand Replacing Fire
0.0032
Stand Replacing Fire
0.0032
a Proportion
Proportiona
To State Acronym
1.00
1.00
GFB
GFB
1.00
1.00
0.55
0.45
0.09
0.91
1.00
GFB
SSO
GFB
SSO
GFB
SSO
GFB
1.00
1.00
0.55
0.45
0.09
0.91
1.00
GFB
SMO
GFB
SMO
GFB
SMO
GFB
1.00
1.00
0.55
0.45
0.09
0.91
1.00
GFB
MVO
GFB
MVO
GFB
MVO
GFB
1.00
1.00
0.02
0.19
0.79
0.84
0.16
0.87
0.13
GFB
SSO
GFB
SSC
SSO
SSC
SSO
GFB
SSO
1.00
1.00
0.02
0.19
0.79
0.84
0.16
0.87
0.13
GFB
SMO
GFB
SMC
SMO
SMC
SMO
GFB
SMO
1.00
1.00
0.02
0.19
0.79
0.84
0.16
0.87
0.13
GFB
MVO
GFB
MVC
MVO
MVC
MVO
GFB
MVO
of acres affected by a transition that will move to the destination state.
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Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona
Table 4b—Pinyon-juniper grassland natural growth in the absence of disturbance
successional transitions expressed as the average annual probability per hectare per
year.
From State Code: Acronym: Description
A: GFB: Grass/Forb/Brush B: SSO: Seed/Sap, Open
C: SMO: Small, Open
D: MVO: Medium to Very Large Open E: SSC: Seed/Sap Closed
F: SMC: Small, Closed
G: MVC: Medium to Very Large Closed
Probability
To State Acronym
.9691
.0136
.0041
.0132
.9249
.0269
.0247
.0236
.0045
.9175
.0193
.0024
.0494
.0070
.0078
.0036
.9714
.0014
.0016
.0142
.9093
.0907
.0004
.9759
.0237
.0003
.0002
.0036
.9960
GFB
SSO
SMO
MVO
SSO
SMO
SSC
SMC
SSO
SMO
MVO
SSC
SMC
MVC
SSO
SMO
MVO
SSC
SMC
MVC
SSC
SMC
SSC
SMC
MVC
MVO
SSC
SMC
MVC
composition and structure from further analysis; thus, we eliminated wildlife
inventories, mine reclamation activities, etc. We summarized the remaining 8,747
management activities into standardized transition classes such as prescribed burning, fuels treatment, and harvest thinning. As with the wildland fire transitions,
we calculated average annual probability-per-hectare values for each PNVT and
assigned these probabilities to each model state (table 4a). In a typical year during the sampled time period, 0.1 percent of the pinyon-juniper grassland PNVT
was affected by these activities.
Insect and disease transitions—Both localized and widespread mortality
events have occurred in the pinyon-juniper woodlands on the Coconino NF (Lynch
and others 2007). These events have typically been pinyon ips outbreaks associated
with periods of drought, such as occurred in the 1950s, and more recently in the
mid-1990s and 2001-2003. Localized outbreaks resulted from range improvement
projects that generated large amounts of fresh pinyon slash (Negrón and Wilson
2003; Yasinski and Pierce 1962). Although pinyon ips outbreaks can be severe,
with pinyon mortality approaching 100 percent within a given stand, they are
generally short lived (1-2 years). The pinyon ips outbreak during the late-1990s
east of Flagstaff near Twin Arrows encompassed almost 5,261 hectares (13,000
acres) at its peak (Negrón and Wilson 2003).
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Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona
Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie
At least within the historic period, the size and severity of the recent droughtand pinyon ips-related die-off is unprecedented for the Coconino NF and northern
Arizona (Allen 2007; Mueller and others 2005). The contemporary pinyon dieoff is 100 times as large (two orders of magnitude) as any previously recorded
acreage for pinyon ips beetle mortality for the Coconino NF, Kaibab NF, and
Grand Canyon National Park. High levels of pinyon mortality were detected by
aerial survey during 2001 through 2003, with approximately 809,389 hectares
(2,000,000 acres) impacted Region-wide and more than 60,704 hectares (150,000
acres) on the Coconino NF. The mortality was primarily attributed to pinyon ips
attacking drought-stressed pinyon; however, twig beetles (Pityophthorus spp.)
were also observed killing smaller pinyon in 2003. Pinyon mortality averaged
41.4 percent within an 80-km (50-mile) radius of Flagstaff, with mortality being
significantly greater on southern aspects and shallow soils developed in volcanic
cinders (Gitlin and others 2006).
Using data from the above insect and disease outbreaks, we calculated average annual probability-per-hectare values for each model state. These transition
probabilities were assigned to each model state (table 4a).
Natural growth transitions in the absence of disturbance—To quantify
the natural growth transitions that will occur in the absence of natural and
anthropogenic disturbances, we used the PRESIDE software (Vandendriesche
2009) to process the outputs of FVS (Dixon 2002). In our case study, we show
the results of applying this methodology in the Southwestern Region. The steps
in this process include:
1. Prepare the FIA inventory data for projection by FVS: Each FIA plot for the
PNVT in the Southwestern Region is assigned to the appropriate model
state.
2. Perform FVS calibration steps: Calibration procedures include using the
FVS self-calibrating feature, estimating and inputting natural regeneration
response, accounting for tree defect for volume estimates, and determining
tree species size attainment and limiting stand maximum density.
3. Run natural growth projections for each FIA plot using the calibrated FVS
to simulate growth over a 250-year time period.
4. Process the tree list output through the PRESIDE post-processor classifier
and accumulate the results into a matrix from which to estimate the average
annual probability per hectare that in the absence of disturbance a plot will
transition from one state to another state (table 4b).
5. Using the sample of plots populating each state at each point in time during
the projection, summarize the vegetation characteristics of each model state
(table 5). The post-processing software indexes the aggregate state classes
to summary values derived from the tree lists and attributes from standard
FVS outputs. Several dozen vegetation characteristics such as stand volume
and stand carbon can be quantified for each model state.
Model runs—Model simulations from VDDT are non-spatial and reflect a
summary of up to 50,000 sample units or cells. For our study, we opted for 1,000
sample units because our earlier work, and work conducted by TNC and LANDFIRE, indicated that this number produced reasonable and consistent projections
(TNC and others 2006). If we increased the number of cells beyond 1000, results
of the analysis would not be significantly changed, but running time would be
increased significantly. In the next step of the modeling process, we initialized the
starting hectares in each state based on current conditions indicated by mid-scale
vegetation mapping data. We ran multiple simulations to estimate the long-term
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Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie
Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona
Table 5—FVS Outputs.
Vegetation Structure Variables:
VDDT State
Dominance Type
Size Class
Canopy Class
Canopy Layers
Stand Age – Overstory
Stand Age – Dominant Story
Total Plot/Activity Count
A _ GFBB _ SSOC _ SMOD _ MVOE _ SSCF _ SMCG _ MVC
PIED PIED PIED PIED PIED PIED PIED
0
1
2
3
1
2
3
0
1
1
1
2
2
2
1
2
1
1
1
1
1
17
76
98
118
97
146
207
0
55
92
130
68
121
196
323
277
317
1222
194 3084 9895
Stand-Stock Variables:
Seedlings/Acre < 1.0” diameter
61
Trees/Acre = 1.0” diameter
206
Basal Area/Acre = 1.0” diameter
11
Quadratic Mean Diameter – Trees = 1.0” diameter
3
Quadratic Mean Diameter – Top 20 percent, diameter 0
Stand Density Index – SDI_Summation
20
Stand Density Index – SDI_Dq
30
Canopy Cover
6
Live – Cubic Feet/Acre
88
Live – Board Feet/Acre
0
Wildlife Habitat Variables:
R3 – Vegetative Structural Stage
Standing Snags
Small = 5-10” diameter
Large = 10”+ diameter
Extra-large = 18”+ diameter
Snag Recruitment
Small = 5-10” diameter
Large = 10”+ diameter
Extra-large = 18”+ diameter
Pestilent Disturbance Variables:
Dwarf Mistletoe Rating
Wildfire Risk Variables:
Crown Bulk Density
Crown Base Height
Crowning Index
Torching Index
Fire Hazard Rating
Fuel Load – Coarse Woody Debris = 0-3” diameter
Fuel Load – Coarse Woody Debris = 3-12” diameter
Fuel Load – Coarse Woody Debris = 12”+ diameter
Biomass-Carbon Variables:
Tree Biomass – Dry weight live & dead/boles & crown
Stand Carbon – Total carbon above & below ground
330
1
148
441
37
4
7
90
106
21
233
5
117
395
44
5
9
94
111
22
328
12
103
286
57
6
14
99
128
21
568
16
265
777
84
5
8
195
221
40
515
30
3ASS
4ASS
5ASS
1C
159
493
117
7
11
233
260
48
1176
18
89
315
135
9
15
235
263
48
1741
11
4CSS
1.1
0.6
2.8
2.4
3.4
26.4
1.6
2.7
2.9
5.9
4.3
6.2
0.7
1.2
1.3
2.8
1.5
2.0
0.2
0.4
2.0
0.9
4.9
26.0
0.5
1.3
1.0
2.5
2.4
3.5
0.2
0.4
0.4
1.0
0.6
0.7
0.04
0.09
0.14
0.15
0.22
0.34
5CSS
14.0
12.1
2.6
11.4
7.5
0.9
0.41
0.00 0.02 0.02
0.01 0.04
0.04
0.03
4.5
4.7
6.2
7.6
4.9
5.7
7.5
170.5 72.4 85.7 121.8 52.3
49.0
61.3
5.0
3.2
6.4
8.0
3.1
5.8
10.1
3.0
3.0
3.0
3.0
3.0
3.0
3.0
0.1
0.4
0.5
0.6
1.2
1.7
1.5
0.2
0.7
1.3
2.1
1.9
5.1
9.4
0.2
0.8
1.3
2.0
1.2
3.1
5.2
2.4
2.8
7.6
7.2
8.6
8.5
14.3
12.4
16.7
14.8
27.8
24.9
36.4
32.6
USDA Forest Service Proceedings RMRS-P-61. 2010.
Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona
Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie
effects of continuing current management under the existing land management
plan. We ran ten simulations with each simulation projecting conditions annually
for 200 years based on data and assumptions described earlier. We compared the
average annual results of these simulations with current conditions and reference
conditions (table 6)
Table 6—Proportion of area in pinyon-juniper grassland states in current and
projected conditions.
19
Percent
departure
34
ECC
2
1
18
23
36
2
1
2
18
17
32
37
40
42
2
2
Vegetation state
A
B
C
D
E
F
G
Current condition
Projected trends
23
1
13
27
0
17
10 years
20
4
9
25
50 years
200 years
15
13
6
4
6
5
22
22
Results
Here we provide results to answer the question, “How do current and projected
conditions compare to reference conditions?” Again, reference conditions were
derived from VDDT models, developed by LANDFIRE, to quantify the historical proportion of major vegetation states of the pinyon-juniper grassland system
(table 2). Current conditions and projected conditions are summarized in table 6.
Current conditions represent existing vegetation mapping synthesized according to the vegetation states contained in the pinyon-juniper grassland model. An
ECC value of 34 indicates a system that is somewhat departed from reference
conditions, on the low end of the moderate range. Current conditions indicate an
uncharacteristic excess of grass-forbs communities (state A), an excess of closed
woodlands (states B and E), and a reciprocal deficit of open woodlands (states
C and D).
Likewise future projections indicate a continuing trend towards departure,
from an index value of 36 at 10 years, to 40 at 50 years. If current management
continues, departure from reference conditions as measured by Ecosystem Condition Class will increase over time due to more acres moving to the closed states.
These results relate to the ecological sustainability concepts stated in our introduction and restated more simply here: Every species around today persisted
over time in its environment under reference conditions. If current and proposed
future management creates or approximates that environment, then the species
is not likely or is less likely to be at risk. On the other hand, as in this case study,
if the ecosystem is departed from reference conditions, and if that departure
increases over time, then both the ecosystem and its associated species are less
likely to be sustainable.
USDA Forest Service Proceedings RMRS-P-61. 2010.
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Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie
Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona
Discussion
The Analysis Process
As mentioned, TNC, LANDFIRE, and others have made a significant investment in the development of reference condition descriptions and models. We’ve
complimented these models with the development of calibrated and more detailed
models that depict current trends and project future conditions. Current and future
conditions can be compared with reference conditions to answer two questions:
1) is there a current departure from reference conditions, and 2) will conditions
remain static, trend towards, or trend away from reference conditions? Trends
away from reference condition may indicate an ecosystem at risk and, if so, the
model can be further tooled to evaluate the potential effectiveness of management strategies.
Also, as discussed under Methods, in retooling the models, several assumptions
were necessary in the face of uncertainties concerning the historic condition.
While additional information is needed to supplement and refine concepts for the
pinyon-juniper grassland PNVT, working assumptions on fire frequency and stand
dynamics were necessary to enable useful modeling of the system. For example,
we assumed that a plurality of tree diameters existed to indicate one of four treedominated states, acknowledging that multiple tree cohorts within any one plant
community were likely due to fire frequency and productivity.
Evaluation of Results
Southwestern pinyon-juniper woodlands span a wide range of environmental
settings over 8.6 million hectares (3.5 million acres), yet historical descriptions
are extremely limited. The pinyon-juniper grassland type is thought to have been
maintained historically by frequent, low-severity surface fires that spread from
and into adjacent systems including semi-desert grassland, juniper grassland,
and ponderosa pine forest. Some references (e.g., Gottfried 2003) suggest that
the resulting stand structure would have been uneven-aged, dominated by open
grown trees. Modern fire suppression and grazing would have since favored
closed canopy structures susceptible to drought-insect induced mortality and
uncharacteristic fire (stand replacement). The current surplus of grass-forbs
communities has likely resulted from stand clearing and pasture development,
and from increased drought-insect mortality and fire activity. Long term VDDT
modeling based on current practices, as reflected in management records from
1988 to 2006, indicates the perpetuation of dense canopies with regular conversion to a grass-forbs state.
The objective of this paper is to illustrate through a case study how State-andTransition Models (STMs) can be developed and used to evaluate the ecological
sustainability of ecosystems. We accomplished this objective by using an empirical
approach to create and calibrate our models based on existing inventory data and
FVS simulations based on existing data; this allowed us to compare and contrast
reference conditions, existing conditions and projected conditions to quantify the
departure of existing and projected conditions from reference conditions.
Our analysis indicates that the pinyon-juniper grassland ecosystem on the
Coconino NF is moderately departed from reference conditions and that this
trend will continue into the future under the existing land management plan. Fire
suppression coupled with infrequent forest management activities contributes
to an existing departure from reference conditions. Thus, the continued current
implementation of the existing land management plan may pose a risk to the
ecological sustainability of this ecosystem.
332
USDA Forest Service Proceedings RMRS-P-61. 2010.
Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona
Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie
Others such as Arno and Fiedler (2005) have explored deteriorated forest and
woodland conditions in western North America and reached similar conclusions.
By developing empirically based landscape dynamics models, we can quantify
woodland conditions with more reliability to assess the ecological sustainability
of these ecosystems within a more credible, systematic framework for strategic
land management plans.
Addressing Carbon Accounting and Climate Change
Future extensions of our methodology include projecting the effects of climate
change on ecological sustainability and providing spatial simulations (Miller
2007). We also advocate evaluating adaptive and mitigation strategies as outlined
by Millar and others (2007).
Carbon accounting for mitigation strategies was provided by the carbon extension of the Forest Vegetation Simulator (Havis and Crookston 2008). Carbon
accounting attributes are shown in table 7. Our results indicate that as the ecosystem moves further away from reference conditions over time (due to more acres
moving into the closed states), the ecosystem sequesters more total carbon (above
and below ground), because the closed states contain more stand carbon per acre
than do the open states. This represents a trade off that must be evaluated by land
managers: managing toward reference conditions versus managing to maximize
short-term carbon sequestration.
The long-term sustainability of these uncharacteristic closed states is dependent
upon insect, drought, and fire occurrence. For example, as closed canopy states
are removed by wildfire, the sequestered carbon is released to the atmosphere.
Sequestration of excess carbon in closed canopy states in frequent-fire adapted
forest types may result in a net long-term loss of carbon sequestration values
when uncharacteristic stand-replacing fires occur (Hurteau and others 2008).
The current model does not provide for charcoal or soil organic carbon, though
future analyses are likely to include these components (DeLuca and Aplet 2008;
Jenkins and others 2003). The amount of soil carbon in the pinyon-juniper woodlands is significant; it can be up to 8 tons per acre in the A horizon and up to
12 tons per acre in the total solum (Meurisse and others 1991). The amount of
organic carbon in soils within the pinyon-juniper woodlands is inherently lower
than higher elevation montane forest types (Meurisse and others 1991).
Adaptation strategies necessitate predictions about future vegetation patterns
and at this time, we are considering the advantages and disadvantages of alternative approaches to modeling climate change. The assumptions in our projection
Table 7—Thousands of tons of stand carbon occurring above and below ground.
Vegetation state
Current condition
Projected trends
A
194
B
22
C
333
D
1,010
E
0
10 years
169
87
231
935
45 1,352 2,262
5,081
50 years
200 years
127
110
130
87
154
128
823
823
45 1,352 3,147
89 1,277 3,639
5,778
6,153
F
G
1,277 1,869
Total
4,705
a
Stand carbon per acre is taken from table 5 and does not reflect charcoal or organic soil
carbon. Acres are taken from table 6.
USDA Forest Service Proceedings RMRS-P-61. 2010.
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Weisz, Triepke, Vandendriesche, Manthei, Youtz, Simon, and Robbie
Evaluating the Ecological Sustainability of a Pinyon-Juniper Grassland Ecosystem in Northern Arizona
models can be modified in the following ways to incorporate the emerging evidence from climate research (Hemstrom and Merzenich, unpublished document):
1. Types of states: Climate change may result in the addition or removal of states
within a PNVT as new vegetation composition and structural patterns are
introduced with changing site potential and processes (such as the introduction of exotic species).
2. Types of transitions: Climate change may result in the addition or removal of
transitions within a PNVT, with novel patterns of vegetation composition,
structure, and process.
3. Rates of transitions: The rates of transitions between model states for existing
transitions, for example, stand replacing fire, may change within the PNVT
and planning area.
4. New (adventive) PNVTs: Adventive PNVTs may need to be modeled, depending on the climate scenario.
5. Transitions between PNVTs: In addition to transitions within PNVT models,
transitions between PNVTs may be necessary to reflect the movement of
area between PNVT classes as climate changes.
6. New management activities: New management activities may be necessary
to respond to adaptive and mitigation strategies (Millar and others 2007),
along with modification to the rates of existing transitions.
7. Projected climate variability: Changes in the annual variation of phenomena
such as wet years, dry years, insect and disease incidence, etc. may be explicitly modeled within existing VDDT software.
8. Addressing multiple climate scenarios: Current assumptions can be modified to reflect each climate change scenario that needs to be considered by
management; for example in scenario 1 the planning area may be getting
warmer and drier, and in scenario 2 the planning area may be getting warmer
and wetter.
Acknowledgments
We would like to thank Melinda Moeur, Jim Merzenich, and Miles Hemstrom
for their inspiration and critical feedback. Also, we would like to thank Leonardo Frid for his thoughtful insights and suggestions. Rory Steinke enhanced our
understanding of the pinyon-juniper terrestrial ecosystem on the Coconino NF.
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for the facts and accuracy of the information presented herein.
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